Optimization of controlled damages on the recognition in the master education
Journal Title: Журнал інженерних наук - Year 2017, Vol 4, Issue 1
Abstract
Information considered extreme learning algorithm is able to study decision support system as part of the automated control system combined heat and power unit with optimized control tolerances recognition. Within the framework of information fusion algorithm optimization studies DSS ACS for signs of recognition expedient design based on categorical model, which is a reflection of sets involved in machine learning and generalization represents a directed graph, where edges are the respective operators transform sets. Algorithm extreme training information DSS is approaching iterative procedure CFE global maximum information to its limit value by optimizing the parameters of DSS. The dependence of the functional efficiency of machine learning DSS’s on the control tolerances on the recognition attributes is established. This value is not high enough CFE DSS training necessitates optimization of other parameters of the study, which affect its functional efficiency. Optimization of control tolerances on recognition features allowed to increase more than twice the value of the informational CFE machine learning DSS.
Authors and Affiliations
M. Bibyk, A. Dovbysh
Keywords
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